A general meta-learning approach that can be applied to any model learned by gradient descent, is presented in [1]}. The goal of this approach is to update the model's parameters using few training steps in order to produce acceptable results on a new task. For this purpose, a parametric model is defined that aims to work well on tasks drawn from a task distribution. The algorithm starts with random initialization of model parameters. During each iteration, a set of tasks are sampled from the given distribution and the tasks' adapted parameters are updated using gradient descent on a particular number of examples. At the end of each iteration, the model's parameters are updated using the adapted parameters. The paper discusses the application of this method in supervised learning and classification, and a possible extension to RL is also explained.
